Univariate data manipulation and plots
Nick Toimieri
sys.date()
1 Triggers and Settings
2 Import fish and manipulate data a bit
Minimum vis for fish analysis = 2-m
Kelp depths = 5 -m Fish depths = 5, 10 -m Inv depths = 5 -m
Visibility data for 2015 are fake. DI was bad and set at 1.0 m; other sites set at 3.0 m
3 FISH
3.1 Rockfish YOY
## `summarise()` has grouped output by 'site', 'year'. You can override using the
## `.groups` argument.
## `summarise()` has grouped output by 'year'. You can override using the
## `.groups` argument.
## Joining, by = "taxa"
3.2 Fish
Fish depth = 5, 10
## Joining, by = "taxa"
3.3 Fish and YOY facet by site x year
## Joining, by = "taxa"
## Joining, by = "taxa"
4 ALGAE
Kelp depth = 5
## `summarise()` has grouped output by 'year', 'site', 'transect', 'species',
## 'zone', 'area'. You can override using the `.groups` argument.
## `summarise()` has grouped output by 'year', 'site', 'species', 'zone', 'area'.
## You can override using the `.groups` argument.
## `summarise()` has grouped output by 'year', 'site', 'species', 'zone'. You can
## override using the `.groups` argument.
## Joining, by = "site"4.1 Nereocystis at Tatoosh
4.2 Nero and Macro
## `summarise()` has grouped output by 'year', 'site', 'zone'. You can override
## using the `.groups` argument.
## `summarise()` has grouped output by 'year', 'site'. You can override using the
## `.groups` argument.
4.3 Big three: Nero, Macro, Ptero
Plot the big 3 species: Macro, Nero, Ptero
4.3.1 Big three 5 m only
4.3.2 Big three - Coastwide
## `summarise()` has grouped output by 'site', 'year'. You can override using the
## `.groups` argument.
## `summarise()` has grouped output by 'year'. You can override using the
## `.groups` argument.
4.3.3 Coastwide 5m only
## `summarise()` has grouped output by 'site', 'year'. You can override using the
## `.groups` argument.
## `summarise()` has grouped output by 'year'. You can override using the
## `.groups` argument.
5 INVERTEBRATES
## `summarise()` has grouped output by 'year', 'site', 'transect', 'observer',
## 'species', 'zone', 'area', 'taxa'. You can override using the `.groups`
## argument.
## `summarise()` has grouped output by 'year', 'site', 'species', 'zone', 'area'.
## You can override using the `.groups` argument.## # A tibble: 6 x 2
## year no
## <dbl> <dbl>
## 1 2015 0
## 2 2016 4
## 3 2017 0
## 4 2018 3
## 5 2019 1
## 6 2021 05.1 Summed Urchins
## `summarise()` has grouped output by 'year', 'site', 'transect', 'observer',
## 'zone', 'area'. You can override using the `.groups` argument.
## `summarise()` has grouped output by 'year', 'site', 'zone', 'area'. You can
## override using the `.groups` argument.
## `summarise()` has grouped output by 'year', 'site', 'transect', 'observer',
## 'area', 'zone'. You can override using the `.groups` argument.
## `summarise()` has grouped output by 'year', 'site', 'zone'. You can override
## using the `.groups` argument.
Max total urchin density at Tatoosh
5-m = 4.0503465 m^-2 10-m = 10.0752194 m^-2
5.2 Urchins by species
Probably worth dropping into the supplement to show that it was mostly purple urchins. Partly interesting because there is no purple urchin fishery, but there is a red one. I think.
## `summarise()` has grouped output by 'year', 'site', 'transect', 'observer',
## 'zone', 'area'. You can override using the `.groups` argument.
## `summarise()` has grouped output by 'year', 'site', 'zone'. You can override
## using the `.groups` argument.
Max total urchin density at Tatoosh
5-m = 3.7336798 m^-2 10-m = 9.5052194 m^-2
## `summarise()` has grouped output by 'site', 'year'. You can override using the
## `.groups` argument.
## `summarise()` has grouped output by 'year'. You can override using the
## `.groups` argument.
Purple urchin density in 2015 = 0.0116667 urchins per m^2 Purple urchin density in 2015 = 1.7637508 urchins per m^2
Increase was 151.1786432 fold.
Purple urchin density in 2015 = 0.7299487 urchins per m^2
Density compared to 2015 was 62.5670286 fold.
5.3 Seastars
## `summarise()` has grouped output by 'year', 'site', 'transect', 'observer',
## 'area', 'zone'. You can override using the `.groups` argument.
## `summarise()` has grouped output by 'year', 'site', 'zone'. You can override
## using the `.groups` argument.
## `summarise()` has grouped output by 'site', 'year'. You can override using the
## `.groups` argument.
## `summarise()` has grouped output by 'year'. You can override using the
## `.groups` argument.
Ugly..no aliby..
## `summarise()` has grouped output by 'year', 'site', 'transect', 'observer',
## 'zone', 'area'. You can override using the `.groups` argument.
## `summarise()` has grouped output by 'year', 'site', 'zone'. You can override
## using the `.groups` argument.
6 Output data for multivariate statistics
6.1 Combine kelp, fish, and inverts for capscale analysis
Not yet transformed to wide format in order to retain SD & SE info upon saving out. Transform in the Multiariate analysis rmd.
Output data files to use for multivariate ordinations.
Use these files and not raw files. Kelp and invert data have been converted to density to account for different transect lengths etc.
## # A tibble: 310 x 33
## # Groups: site, year, area, zone, transect [310]
## year site transect area zone AUFL BAIT CHNU COTT EMBI GOBI HEXA
## <dbl> <fct> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2016 Destruc~ 1 N 10 0 0 0 0 0 0 1
## 2 2016 Destruc~ 2 N 10 0 0 0 0 0 0 3
## 3 2016 Destruc~ 3 N 10 0 0 0 0 0 0 1
## 4 2017 Destruc~ 1 N 5 0 0 0 0 13 0 1
## 5 2017 Destruc~ 4 N 5 0 0 0 0 2 0 1
## 6 2017 Destruc~ 1 S 5 0 0 0 0 1 0 0
## 7 2017 Destruc~ 2 S 5 0 0 0 0 4 0 1
## 8 2019 Destruc~ 1 N 5 0 0 0 0 2 0 6
## 9 2019 Destruc~ 2 N 5 0 0 0 0 0 0 14
## 10 2019 Destruc~ 3 N 5 0 0 0 0 6 0 9
## # ... with 300 more rows, and 21 more variables: OPEL <dbl>, PHOL <dbl>,
## # RIMU <dbl>, RYOY <dbl>, SCMA <dbl>, SEBYTyoy <dbl>, SECA <dbl>,
## # SECAyoy <dbl>, SEFL <dbl>, SEMA <dbl>, SEMAyoy <dbl>, SEME <dbl>,
## # SEMY <dbl>, SEMYyoy <dbl>, SENE <dbl>, SENEyoy <dbl>, SEPI <dbl>,
## # SEPIyoy <dbl>, TOTyoy <dbl>, UNID <dbl>, SEBYT <dbl>## # A tibble: 310 x 18
## # Groups: year, site, transect, zone, area [310]
## year site transect area zone AGAFIM ALAMAR COSCOS CYMTRI DESSPP LAMSET
## <dbl> <fct> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2015 Neah Bay 1 D 5 0 0 0 0 0 0
## 2 2015 Neah Bay 2 D 5 0 0 0 0 0 0
## 3 2015 Tatoosh~ 1 D 5 0 0 0 0 0 0
## 4 2015 Tatoosh~ 2 D 5 0 0 0 0 0 0
## 5 2015 Cape Al~ 1 D 5 0 0 0 0 0 0
## 6 2015 Cape Al~ 2 D 5 0 0 0 0 0 0
## 7 2015 Cape Jo~ 1 D 5 0 0 0 0 0 0
## 8 2015 Cape Jo~ 2 D 5 0 0 0 0 0 0
## 9 2015 Destruc~ 1 D 5 0 0 0.133 0 0 0
## 10 2015 Destruc~ 2 D 5 0 0 0.025 0 0.0167 0.0167
## # ... with 300 more rows, and 7 more variables: MACPYR <dbl>, NERLUE <dbl>,
## # NO_ALG <dbl>, PLEGAR <dbl>, PTECAL <dbl>, SACGRO <dbl>, SACLAT <dbl>## # A tibble: 310 x 30
## # Groups: year, site, transect, zone, area [310]
## year site transect area zone anemone barnacle bivalve `blood star`
## <dbl> <fct> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2015 Neah Bay 1 D 5 0 0 0 0.183
## 2 2015 Neah Bay 2 D 5 0 0 0 0.167
## 3 2015 Tatoosh Isl~ 1 D 5 0.00625 0 0.00556 0.05
## 4 2015 Tatoosh Isl~ 2 D 5 0.0125 0 0.0167 0.05
## 5 2015 Cape Alava 1 D 5 0 0 0.00370 0
## 6 2015 Cape Alava 2 D 5 0 0 0 0.0667
## 7 2015 Cape Johnson 1 D 5 0 0 0 0
## 8 2015 Cape Johnson 2 D 5 0.00208 0 0.00556 0
## 9 2015 Destruction~ 1 D 5 0 0 0 0.0333
## 10 2015 Destruction~ 2 D 5 0.00833 0 0 0.167
## # ... with 300 more rows, and 21 more variables: `brood star` <dbl>,
## # chiton <dbl>, crabs <dbl>, cucumber <dbl>, `green urchin` <dbl>,
## # hermit_crabs <dbl>, `kelp crab` <dbl>, `large star` <dbl>,
## # `leather star` <dbl>, `medium star` <dbl>, nudibranch <dbl>,
## # Pisaster <dbl>, `purple urchin` <dbl>, Pycnopodia <dbl>,
## # `red urchin` <dbl>, sea_star_YOY <dbl>, `shelled gastropod` <dbl>,
## # `shelled mussel` <dbl>, sponge <dbl>, tunicate <dbl>, `NA` <dbl>## `summarise()` has grouped output by 'year'. You can override using the
## `.groups` argument.
## `summarise()` has grouped output by 'year'. You can override using the
## `.groups` argument.
## `summarise()` has grouped output by 'year'. You can override using the
## `.groups` argument.## # A tibble: 30 x 5
## # Groups: Year [6]
## Year Site Fish Kelp Invertebrates
## <dbl> <fct> <dbl> <dbl> <dbl>
## 1 2015 Cape Johnson 2 2 2
## 2 2015 Cape Alava 2 2 2
## 3 2015 Tatoosh Island 2 2 2
## 4 2015 Neah Bay 2 2 2
## 5 2015 Destruction Island NA 2 2
## 6 2016 Destruction Island 3 7 7
## 7 2016 Cape Johnson 10 13 13
## 8 2016 Cape Alava 12 10 10
## 9 2016 Tatoosh Island 8 9 9
## 10 2016 Neah Bay 10 10 10
## # ... with 20 more rows6.2
combind fish and kelp in wide format using fish COUNT data. include area info for fish and kelp
## `summarise()` has grouped output by 'site', 'year', 'zone', 'area', 'transect'.
## You can override using the `.groups` argument.
## `summarise()` has grouped output by 'site', 'year', 'zone', 'area'. You can
## override using the `.groups` argument.## [1] 82 32## [1] 4023## `summarise()` has grouped output by 'site', 'year', 'area', 'zone'. You can
## override using the `.groups` argument.## [1] 100 18## Joining, by = c("site", "year", "zone", "area")## [1] 82 467 Quick view of what species to include in kelp ordinations. Basically only the big three have any abundance.
7.1 Algae
## `summarise()` has grouped output by 'species'. You can override using the
## `.groups` argument.
7.2 Fish
Note, the y-axes are very different
## `summarise()` has grouped output by 'taxa'. You can override using the
## `.groups` argument.
7.2.1 NOTES
for multivariate analysis; cut and paste to that file
fish.spp = c(“OPEL”, “HEXA”, “SECA”, “SCMA” ,“SENE”, “SEME”, ‘EMBI’,‘GOBI’)
yoy.spp = c(“SECAyoy”, “SEPIyoy”, “SEMYyoy”, “SEBYTyoy”,“RYOY”)
8 SOME USEFUL TABLES
## `summarise()` has grouped output by 'species'. You can override using the
## `.groups` argument.9 A tibble: 6 x 5
Species Common name size_class Group Total
## # A tibble: 6 x 3
## Species Density SD
## <chr> <dbl> <dbl>
## 1 Pterygophora californica 1.11 1.38
## 2 Nereocystis luetkeana 0.874 1.70
## 3 Macrocystis pyrifera 0.554 1.37
## 4 Laminaria setchellii 0.132 0.401
## 5 Saccharina dentigera 0.0877 0.288
## 6 Pleurophycus gardneri 0.0862 0.308## # A tibble: 6 x 4
## Species Taxa Density SD
## <chr> <chr> <dbl> <dbl>
## 1 Balanus nubilis barnacle 0.829 3.67
## 2 Strongylocentrotus purpuratus purple urchin 0.714 2.40
## 3 Nucella lamellosa shelled gastropod 0.293 1.36
## 4 Mesocentrotus franciscanus red urchin 0.160 0.600
## 5 Cucumaria miniata cucumber 0.107 0.159
## 6 Henricia leviuscula blood star 0.0960 0.091010 Kelp Canopy
Imports data file from other R file. Plot just kelp canopy. Add to ggarrange below.
## Scale for 'x' is already present. Adding another scale for 'x', which will
## replace the existing scale.
Mean ha for 2004-2013: 700.6700019 ha +/- 43.6630069 s.e.
Total canopy 2014: 337.2999981 ha
Drop from previous decade: 0.4813964 % of previous cover.
Mean ha for 2015-2020:659.7333332 ha +/- 33.9434596 s.e.
Compared to earlier decade: 0.941575 % of previous cover.
Totals 2014 for Total, Nereo, Macro = 337.2999981, 89.6000009, 247.8999973 ha Totals 2020 for Total, Nereo, Macro = 701.8, 383.4, 318.6 ha
Change was 2.0806404, 4.2790178, 1.2851957 fold.
11 Combined Figures for MS
## Scale for 'y' is already present. Adding another scale for 'y', which will
## replace the existing scale.
## Scale for 'y' is already present. Adding another scale for 'y', which will
## replace the existing scale.
12 Combined Kelp and Temperature figure
## [1] "1" "2" "3" "4" "5" "6" "7" "8" "9" "10"## Note: Using an external vector in selections is ambiguous.
## i Use `all_of(sites2)` instead of `sites2` to silence this message.
## i See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
## This message is displayed once per session.
## `summarise()` has grouped output by 'year'. You can override using the `.groups` argument.
## Scale for 'x' is already present. Adding another scale for 'x', which will
## replace the existing scale.
